Vision and Language Model

Vision-and-language models (VLMs) aim to integrate visual and textual information, enabling machines to understand and reason about the world in a more human-like way. Current research focuses on improving VLMs' semantic understanding, particularly addressing their sensitivity to lexical variations and compositional reasoning limitations, often through techniques like prompt tuning and knowledge distillation across different model architectures. These advancements are crucial for enhancing applications such as visual question answering, image captioning, and visual navigation, while also raising important considerations regarding bias mitigation and the development of safer, more robust systems.